A. Field of the Invention
The present disclosure generally relates to information sharing. The disclosed embodiments relate more specifically to a system, apparatus, and method for identifying related content based on eye movements.
B. Related Technology
With advances in technology and network capabilities, people have access to more information than ever before. But if massive amounts of information are communicated to users directly, those users may experience information overload and either ignore or fail to recognize useful information. On the other hand, if users are left to search and find information for themselves, they may be unable to effectively find helpful information among the overabundance of available information. Accordingly, it is difficult for users to utilize that overabundance of information efficiently.
In response, software applications have been developed that extract keywords from documents, web pages, and e-mails using summarization and keyword spotting algorithms. Those techniques use word frequency, part of speech, and other formal properties of the text, or the semantics of a specified domain, to determine a candidate list of keywords. The purpose of that keyword extraction is typically to semantically index documents for later retrieval using keyword-based search algorithms. However, the keywords identified with those techniques and algorithms are based on the content of documents, web pages, and e-mails, as a whole. But in long documents, web pages, and e-mails; or across many documents, websites, and e-mails that a user is concurrently viewing: not all of the content may be relevant to the user. Accordingly, the keywords extracted using conventional techniques and algorithms are more likely to reflect the interests of the author of the content than the interests of the reader of the content.
Similarly, software applications have been developed that use various forms of implicit feedback to identify areas of interest to users. Those software applications evaluate such things as click-stream data, time spent during reading, amount of scrolling, and exit behavior to help in predicting potential areas of interest for that user. Nevertheless, those software applications offer limited information about a user's actual interests and intentions because they do not take into account the content on which the user actually focused.